A business-automation system may store data related to a customer in a user profile. Data in such a profile may be organized as a simple flat table, as a hierarchical data structure, or distributed among multiple, linked, data structures that may themselves be distributed among multiple systems. In some cases, a user profile may span one or more complex objects, at least one of which may comprise both data and logic.
Information represented by a user profile may be time-sensitive or space-sensitive. A profile might, for example, identify a seasonal client's account that is more active during certain times of the year. Marketing to such an account might thus be more efficient and effective when taking into account this time-sensitive characteristic of the client.
Similarly, some user profiles may be associated with specific geographical or geospatial locations. A social-network user who resides in a coastal city, for example, would be a better candidate for a line of surfer's accessories than she would have been when previously living in a mountainous, landlocked region.
Present solutions do not provide a standard framework that allows a business to model, instantiate, store, manage, and retrieve such user profiles as functions of time- and space-sensitive parameters. Such limitations are particularly troublesome in modern multi-tenant virtual environments, distributed and enterprise networks, cellular networks, and cloud-computing platforms. Such environments serve enormous numbers of users that may be located anywhere in the world and that may each be associated with time-sensitive or space-sensitive characteristics.
Existing solutions excel at storing, analyzing, and retrieving huge volumes of user data in a single dimension, as a single-parameter function of a particular type of subject matter, such as an interest in a particular product line. Some methods may even aggregate data from multiple sources in order to present multiple views of a user.
But even these approaches are generally constrained by resource limitations that prevent them from accounting for ad hoc effects of dynamic time-sensitive or location-sensitive factors. Within such a relatively static framework, a profile may, for example, associate a user with a particular subject, but would incur an impractical degree of overhead if it attempted to derive insight from such an association as a function of ad hoc or evolving time- and space-sensitive context. Traditional methods thus cannot efficiently provide such multi-dimensional solutions that provide accurate results with a high level of confidence.